Patient navigation programs for people with dementia, their caregivers, and members of their care team: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to map the literature on the characteristics, barriers, and faciliators of patient navigation programs for people with dementia, their caregivers, and/or members of their care team across all settings. INTRODUCTION: Patient navigation refers to a model of care that helps guide people through the health care system, matching their unmet needs to appropriate resources, services, and programs. Patient navigation may be beneficial to people with dementia because this is a population that frequently faces fragmented and uncoordinated care and has individualized care needs. INCLUSION CRITERIA: This review will focus on patient navigation programs for people living with dementia, their caregivers, and/or members of their care team, while excluding programs that do not explicitly focus on dementia. It will include patient navigation across all settings, delivered in all formats, and administered by all types of navigators, as long as the program is aligned with this article's definition of patient navigation, while excluding case management. METHODS: This review will be conducted in accordance with JBI methodology for scoping reviews. The MEDLINE, CINAHL, PsycINFO, Embase, and ProQuest Nursing and Allied Health databases will be searched for published articles. Two independent reviewers will screen articles for relevance against the inclusion criteria. The results will be presented in a Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews flow diagram, and the extracted data will be presented in both tabular and narrative format.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.114 | 0.078 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.015 | 0.016 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.063 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".